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That A-10 drop is pure orbital mechanics in atmosphere 📉 Jet's computer solves CCRP, release point = velocity + altitude + drag, same as de-orbit calculation This CBU casing spins at 1000ft & scatters 200+ bomblets over 2 football fields, zero guidance, just angular momentum 💥🛰️

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Profilbild von TrashMammal
TrashMammalvor 4 Tagen

Just think you just watched someones collage tuition fund blow up for what. To test a weapon to be used to kill brown kids over seas.

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The Cosmos Networkvor 4 Tagen

👍🏻

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Noor Rahmanvor 3 Tagen

لما الفيزياء تشتغل مع التكنولوجيا، حتى أبسط حركة في الجو تصير درس في الدقة والهندسة

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🚨 THIS IS INSANE… A UFO JUST HIT 4,300 MPH 🚨 The Trump White House just dropped a BOMBSHELL on UAP disclosure and the skies just got a lot more terrifying. Military witnesses are reporting silent black triangles the size of football fields hovering over nuclear sites… and one craft just got clocked at FOUR THOUSAND THREE HUNDRED MILES PER HOUR. No wings. No engines. No sound. Just pure, physics-defying speed that would turn any fighter jet into scrap metal in a split second. This isn’t swamp gas. This isn’t a weather balloon. This isn’t “ours.” Stephen Miller is personally running point. NDAs are being waived. Whistleblowers are being told: come forward. Now. People have taken this secret to their graves for decades. That era is OVER. Something is flying in our skies at speeds that shouldn’t even be possible… and for the first time, the people in power actually want the truth. The bipartisan UAP Disclosure Act is exploding with momentum because of this. If that 4,300 mph number doesn’t make your stomach drop… nothing will. Pay attention. This is bigger than any of us. 👀

CONSTITUTION X 🇺🇸

96,715 Aufrufe • vor 2 Monaten

"Superfluid Vortices as a Model for Gravitational Precession" with Implications for Electromagnetism Authors: Matt Baker, Alan Baker Superfluid vortices in a Bose-Einstein condensate provide a framework for modeling gravitational effects, particularly the precession of planetary orbits, building on concepts of superfluidity in quantum systems. Calculations using modified Navier-Stokes equations and angular momentum terms for vortex dynamics predict the orbital precession of Venus, Earth, and Mars, matching observed data within one, two, and three arc seconds per century, respectively, employing a superfluid of sub-quark-scale particles, below 10^-15 meters, which remain undetectable due to their infinitesimal size and ability to flow through detection instruments unimpeded by thermal interactions. The theoretical framework employs Bernoulli’s principle, where large, slow-spinning vortices create low-pressure zones, producing a gravitational-like pull over long distances. At sub-electromagnetic scales, below 10^-17 meters, these vortices operate without directly interacting with electromagnetic fields, yet their rapid dynamics could indirectly influence such interactions. Analysis indicates that tighter, faster vortices generate localized forces resembling electromagnetic interactions, with the work done scaling similarly to gravitational effects but over much shorter distances, suggesting a unified mechanism across scales. Experimental validation is proposed through a Superfluid Vortex Collider (SFVC), where quantized vortices in a Bose-Einstein condensate at near-absolute-zero temperatures would be collided. Predictions indicate that microvortices from these collisions could replicate gravitational interactions, with quantum coherence in Bose-Einstein condensates, as demonstrated (Jin et al., 1996), supporting the feasibility of such experiments. Superfluid vortex dynamics offer a model for gravity with potential extensions to electromagnetism. The increasing discrepancies with orbital distance suggest a distance-dependent vortex parameter, alongside variations in vortex speed, angular momentum, effective viscosity, or planetary density, which could be refined to further align predictions with observations. Future investigations could explore vortex entanglement in ultra-coherent conditions, though achieving such conditions remains challenging. References L. P. Pitaevskii and S. Stringari, Bose-Einstein Condensation and Superfluidity (Oxford University Press, 2016). D. S. Jin, J. R. Ensher, M. R. Matthews, C. E. Wieman, and E. A. Cornell, Collective excitations of a Bose-Einstein condensate in a dilute atomic gas, Phys. Rev. Lett. 77, 420 (1996). Appendix: Mathematical Derivations To model planetary precession, modified Navier-Stokes equations for superfluids were employed, incorporating angular momentum terms for vortex dynamics. For a vortex with circulation \(\Gamma\), the velocity field \(v(r) = \Gamma/(2\pi r)\) creates a low-pressure zone via Bernoulli’s principle, yielding a gravitational-like force scaling as \(F \propto 1/r^2\). Orbital precession was calculated using the angular momentum contribution \(L = mvr\), adjusted for vortex-induced perturbations, resulting in precession rates of 43 arc seconds per century for Venus (observed: 42 arc seconds), 3.8 arc seconds per century for Earth (observed: 5.0 arc seconds), and 1.4 arc seconds per century for Mars (observed: 4.0 arc seconds). Discrepancies may stem from variations in vortex speed \(\Gamma\) or effective planetary density \(\rho\). For electromagnetic interactions, tighter vortices with higher angular velocity \(\omega\) were analyzed. The force generated scales as \(F \propto \omega^2/r^2\), resembling electromagnetic interactions at scales of 10^-17 meters. The work done, integrated over distance, yields a ratio comparable to gravitational work at larger scales, supporting a unified force model.
2:05

"Superfluid Vortices as a Model for Gravitational Precession" with Implications for Electromagnetism Authors: Matt Baker, Alan Baker Superfluid vortices in a Bose-Einstein condensate provide a framework for modeling gravitational effects, particularly the precession of planetary orbits, building on concepts of superfluidity in quantum systems. Calculations using modified Navier-Stokes equations and angular momentum terms for vortex dynamics predict the orbital precession of Venus, Earth, and Mars, matching observed data within one, two, and three arc seconds per century, respectively, employing a superfluid of sub-quark-scale particles, below 10^-15 meters, which remain undetectable due to their infinitesimal size and ability to flow through detection instruments unimpeded by thermal interactions. The theoretical framework employs Bernoulli’s principle, where large, slow-spinning vortices create low-pressure zones, producing a gravitational-like pull over long distances. At sub-electromagnetic scales, below 10^-17 meters, these vortices operate without directly interacting with electromagnetic fields, yet their rapid dynamics could indirectly influence such interactions. Analysis indicates that tighter, faster vortices generate localized forces resembling electromagnetic interactions, with the work done scaling similarly to gravitational effects but over much shorter distances, suggesting a unified mechanism across scales. Experimental validation is proposed through a Superfluid Vortex Collider (SFVC), where quantized vortices in a Bose-Einstein condensate at near-absolute-zero temperatures would be collided. Predictions indicate that microvortices from these collisions could replicate gravitational interactions, with quantum coherence in Bose-Einstein condensates, as demonstrated (Jin et al., 1996), supporting the feasibility of such experiments. Superfluid vortex dynamics offer a model for gravity with potential extensions to electromagnetism. The increasing discrepancies with orbital distance suggest a distance-dependent vortex parameter, alongside variations in vortex speed, angular momentum, effective viscosity, or planetary density, which could be refined to further align predictions with observations. Future investigations could explore vortex entanglement in ultra-coherent conditions, though achieving such conditions remains challenging. References L. P. Pitaevskii and S. Stringari, Bose-Einstein Condensation and Superfluidity (Oxford University Press, 2016). D. S. Jin, J. R. Ensher, M. R. Matthews, C. E. Wieman, and E. A. Cornell, Collective excitations of a Bose-Einstein condensate in a dilute atomic gas, Phys. Rev. Lett. 77, 420 (1996). Appendix: Mathematical Derivations To model planetary precession, modified Navier-Stokes equations for superfluids were employed, incorporating angular momentum terms for vortex dynamics. For a vortex with circulation \(\Gamma\), the velocity field \(v(r) = \Gamma/(2\pi r)\) creates a low-pressure zone via Bernoulli’s principle, yielding a gravitational-like force scaling as \(F \propto 1/r^2\). Orbital precession was calculated using the angular momentum contribution \(L = mvr\), adjusted for vortex-induced perturbations, resulting in precession rates of 43 arc seconds per century for Venus (observed: 42 arc seconds), 3.8 arc seconds per century for Earth (observed: 5.0 arc seconds), and 1.4 arc seconds per century for Mars (observed: 4.0 arc seconds). Discrepancies may stem from variations in vortex speed \(\Gamma\) or effective planetary density \(\rho\). For electromagnetic interactions, tighter vortices with higher angular velocity \(\omega\) were analyzed. The force generated scales as \(F \propto \omega^2/r^2\), resembling electromagnetic interactions at scales of 10^-17 meters. The work done, integrated over distance, yields a ratio comparable to gravitational work at larger scales, supporting a unified force model.

Matt Baker

33,215 Aufrufe • vor 1 Jahr

2034 Earth–Venus–Mars opportunity looks promising. 10–15 on-orbit refueling operations may be needed to make a crewed ship full. Most can be done at an altitude of 180–200 km, made possible by Starship’s size. The final refueling may be performed at a higher altitude of ~2000 km, just below the Van Allen belt. Earth departure on 2034-08-21 from 2000 km orbit. A Trans-Venus Injection burn of ~3.7 km/s will place the ship on an Earth–Venus–Earth free-return trajectory. Venus flyby is expected on 2034-12-19, 120 days after departure. Two weeks before the encounter, if the mission proceeds as planned, a 25-m/s maneuver will shift the trajectory from Earth-return to Mars-bound. If not, the ship will free return to Earth in September 2035. The Venus gravity assist will send the ship into another Earth free-return trajectory, with Mars flyby around 2035-06-02. One week before reaching Mars, a system health check will determine whether to commit to Mars Orbit Insertion. If it’s GO, a small 10-m/s manuever will put the ship to less than 100 km altitude periapsis. Otherwise, a Mars flyby will lead to an Earth return in May 2036. The ship will enter the Martian atmosphere at about 9.4 km/s, performing an aerobrake to slow to 4.88 km/s and capture into a 100x140000 km, 7-day period high elliptical orbit. At apoapsis, a 50-m/s plane change will align the inclination with Mars’ equator, followed by additional aerobraking to remove about 650 m/s of velocity, placing the spacecraft in a 120x6128 km orbit. A 550-m/s burn at 6128 km altitude will then adjust the trajectory into Phobos orbit. The ship will stay at Phobos for about 7 days. The Mars–Phobos L1 point is only about two miles above Phobos’ surface, and Mars would dominate nearly half the sky, appearing about 80 times larger than the Moon from Earth. The ship will depart for Deimos afterward. Two burns totaling roughly 750 m/s will transfer the ship from Phobos to Deimos. And the ship will stay at Deimos for 7 days more. From Deimos, the ship will raise its apoapsis to form a 20000x140000 km altitude, 7-day orbit, requiring about 420 m/s of delta-v. At apogee, a 50-m/s burn will adjust inclination and lower periapsis to ~500 km for final Trans-Earth Injection. If time and propellant allow, the orbit can be aligned to a polar inclination for Mars ice-cap observations before departure. A Trans-Earth Injection burn at 500 km altitude, requiring 1.5–1.6 km/s of delta-v in early July 2035. If departure on the first days in July, Earth arrival is expected in December 2035. If missed that window, a March 2036 arrival may look more feasible. Nominal mission duration: 490 days, with 30 days in Mars orbit and 14 days at Phobos and Deimos. Two planets, two moons for 3.7+0.025+0.010+0.05+0.42+0.55+0.75+1.55=7.06 km/s Δv
0:24

2034 Earth–Venus–Mars opportunity looks promising. 10–15 on-orbit refueling operations may be needed to make a crewed ship full. Most can be done at an altitude of 180–200 km, made possible by Starship’s size. The final refueling may be performed at a higher altitude of ~2000 km, just below the Van Allen belt. Earth departure on 2034-08-21 from 2000 km orbit. A Trans-Venus Injection burn of ~3.7 km/s will place the ship on an Earth–Venus–Earth free-return trajectory. Venus flyby is expected on 2034-12-19, 120 days after departure. Two weeks before the encounter, if the mission proceeds as planned, a 25-m/s maneuver will shift the trajectory from Earth-return to Mars-bound. If not, the ship will free return to Earth in September 2035. The Venus gravity assist will send the ship into another Earth free-return trajectory, with Mars flyby around 2035-06-02. One week before reaching Mars, a system health check will determine whether to commit to Mars Orbit Insertion. If it’s GO, a small 10-m/s manuever will put the ship to less than 100 km altitude periapsis. Otherwise, a Mars flyby will lead to an Earth return in May 2036. The ship will enter the Martian atmosphere at about 9.4 km/s, performing an aerobrake to slow to 4.88 km/s and capture into a 100x140000 km, 7-day period high elliptical orbit. At apoapsis, a 50-m/s plane change will align the inclination with Mars’ equator, followed by additional aerobraking to remove about 650 m/s of velocity, placing the spacecraft in a 120x6128 km orbit. A 550-m/s burn at 6128 km altitude will then adjust the trajectory into Phobos orbit. The ship will stay at Phobos for about 7 days. The Mars–Phobos L1 point is only about two miles above Phobos’ surface, and Mars would dominate nearly half the sky, appearing about 80 times larger than the Moon from Earth. The ship will depart for Deimos afterward. Two burns totaling roughly 750 m/s will transfer the ship from Phobos to Deimos. And the ship will stay at Deimos for 7 days more. From Deimos, the ship will raise its apoapsis to form a 20000x140000 km altitude, 7-day orbit, requiring about 420 m/s of delta-v. At apogee, a 50-m/s burn will adjust inclination and lower periapsis to ~500 km for final Trans-Earth Injection. If time and propellant allow, the orbit can be aligned to a polar inclination for Mars ice-cap observations before departure. A Trans-Earth Injection burn at 500 km altitude, requiring 1.5–1.6 km/s of delta-v in early July 2035. If departure on the first days in July, Earth arrival is expected in December 2035. If missed that window, a March 2036 arrival may look more feasible. Nominal mission duration: 490 days, with 30 days in Mars orbit and 14 days at Phobos and Deimos. Two planets, two moons for 3.7+0.025+0.010+0.05+0.42+0.55+0.75+1.55=7.06 km/s Δv

Chun

225,552 Aufrufe • vor 1 Jahr

Warmup to Statistical Mechanics What Exactly is a Hamiltonian A System? In ordinary Mechanics, you might begin with position and velocity. Hamiltonian Mechanics rewrites the same motion in a different language. Instead of position and velocity, it uses position and momentum. We write the position variables as q and the momentum variables as p. Then the full state of the system at one instant is (q, p) That pair is one point in phase space. Why do we do this? Because in these variables, the equations of motion take a remarkably clean form. Everything is generated by one single function, the Hamiltonian H(q, p) and in the simplest cases this Hamiltonian is just the total energy written in terms of position and momentum. So if you know H, you know the dynamics. You might wonder, but how can one function generate motion? The rule is dqᵢ/dt = ∂H/∂pᵢ dpᵢ/dt = −∂H/∂qᵢ These are Hamilton’s equations. Now read them slowly 😄 The rate of change of position comes from differentiating H with respect to momentum. The rate of change of momentum comes from differentiating H with respect to position, with a minus sign. This constitutes the whole engine. A simple example makes this less abstract: Take one particle of mass m moving in a potential V(q). Then the Hamiltonian is H(q, p) = p²/(2m) + V(q) The first term is kinetic energy. The second term is potential energy. Now apply Hamilton’s equations. First, dq/dt = ∂H/∂p = p/m So momentum tells you how position changes. Second, dp/dt = −∂H/∂q = −dV/dq Thus, momentum changes because of force. If you now combine these two equations, you recover ordinary Newtonian mechanics. Since p = m dq/dt, we get m d²q/dt² = −dV/dq So, Hamiltonian mechanics is not a different theory. It is the same mechanics, written in a form that exposes its geometric structure much more clearly. The animation The full 3D surface is the Hamiltonian itself, the energy landscape H(q, p). The floor underneath is phase space, marked by energy contours and the local flow field. The bright moving point is one actual state (q(t), p(t)) evolving under Hamilton’s equations. Its trail shows that the motion is not arbitrary. It is guided everywhere by the geometry of the same single function H. The render is doing more than illustrating a particle moving, it is showing how one function organizes the whole phase-space motion. The math breakdown: Start with one degree of freedom. The state is described by position q and momentum p. So the system lives in a two-dimensional phase space with coordinates (q, p) Now choose a Hamiltonian H(q, p) Think of H as the energy function. In many standard systems, H(q, p) = kinetic energy + potential energy For a particle of mass m in a potential V(q), this becomes H(q, p) = p²/(2m) + V(q) Hamilton’s equations say dq/dt = ∂H/∂p dp/dt = −∂H/∂q Now substitute this specific H. First compute the p derivative: ∂H/∂p = ∂/∂p (p²/(2m) + V(q)) = p/m So dq/dt = p/m Now compute the q derivative: ∂H/∂q = ∂/∂q (p²/(2m) + V(q)) = dV/dq So dp/dt = −dV/dq These two first-order equations completely determine the motion. Now, connect this back to Newton’s law. From dq/dt = p/m we get p = m dq/dt Differentiate both sides with respect to time: dp/dt = m d²q/dt² But Hamilton’s second equation gives dp/dt = −dV/dq So , together they imply m d²q/dt² = −dV/dq This is exactly Newton’s second law for motion in the potential V(q). Thus, Hamilton’s equations do not replace mechanic, they reorganize it. #HamiltonianMechanics #PhaseSpace #ClassicalMechanics #MathematicalPhysics #DifferentialEquations #Mathematics #Physics
1:00

Warmup to Statistical Mechanics What Exactly is a Hamiltonian A System? In ordinary Mechanics, you might begin with position and velocity. Hamiltonian Mechanics rewrites the same motion in a different language. Instead of position and velocity, it uses position and momentum. We write the position variables as q and the momentum variables as p. Then the full state of the system at one instant is (q, p) That pair is one point in phase space. Why do we do this? Because in these variables, the equations of motion take a remarkably clean form. Everything is generated by one single function, the Hamiltonian H(q, p) and in the simplest cases this Hamiltonian is just the total energy written in terms of position and momentum. So if you know H, you know the dynamics. You might wonder, but how can one function generate motion? The rule is dqᵢ/dt = ∂H/∂pᵢ dpᵢ/dt = −∂H/∂qᵢ These are Hamilton’s equations. Now read them slowly 😄 The rate of change of position comes from differentiating H with respect to momentum. The rate of change of momentum comes from differentiating H with respect to position, with a minus sign. This constitutes the whole engine. A simple example makes this less abstract: Take one particle of mass m moving in a potential V(q). Then the Hamiltonian is H(q, p) = p²/(2m) + V(q) The first term is kinetic energy. The second term is potential energy. Now apply Hamilton’s equations. First, dq/dt = ∂H/∂p = p/m So momentum tells you how position changes. Second, dp/dt = −∂H/∂q = −dV/dq Thus, momentum changes because of force. If you now combine these two equations, you recover ordinary Newtonian mechanics. Since p = m dq/dt, we get m d²q/dt² = −dV/dq So, Hamiltonian mechanics is not a different theory. It is the same mechanics, written in a form that exposes its geometric structure much more clearly. The animation The full 3D surface is the Hamiltonian itself, the energy landscape H(q, p). The floor underneath is phase space, marked by energy contours and the local flow field. The bright moving point is one actual state (q(t), p(t)) evolving under Hamilton’s equations. Its trail shows that the motion is not arbitrary. It is guided everywhere by the geometry of the same single function H. The render is doing more than illustrating a particle moving, it is showing how one function organizes the whole phase-space motion. The math breakdown: Start with one degree of freedom. The state is described by position q and momentum p. So the system lives in a two-dimensional phase space with coordinates (q, p) Now choose a Hamiltonian H(q, p) Think of H as the energy function. In many standard systems, H(q, p) = kinetic energy + potential energy For a particle of mass m in a potential V(q), this becomes H(q, p) = p²/(2m) + V(q) Hamilton’s equations say dq/dt = ∂H/∂p dp/dt = −∂H/∂q Now substitute this specific H. First compute the p derivative: ∂H/∂p = ∂/∂p (p²/(2m) + V(q)) = p/m So dq/dt = p/m Now compute the q derivative: ∂H/∂q = ∂/∂q (p²/(2m) + V(q)) = dV/dq So dp/dt = −dV/dq These two first-order equations completely determine the motion. Now, connect this back to Newton’s law. From dq/dt = p/m we get p = m dq/dt Differentiate both sides with respect to time: dp/dt = m d²q/dt² But Hamilton’s second equation gives dp/dt = −dV/dq So , together they imply m d²q/dt² = −dV/dq This is exactly Newton’s second law for motion in the potential V(q). Thus, Hamilton’s equations do not replace mechanic, they reorganize it. #HamiltonianMechanics #PhaseSpace #ClassicalMechanics #MathematicalPhysics #DifferentialEquations #Mathematics #Physics

Mathelirium

50,730 Aufrufe • vor 5 Monaten

Elon Musk says every job done at a computer is going like lightning. The last time this happened, the people it happened to came out ahead. Musk: “Anything that is digital, which is like just someone at a computer doing something, AI is going to take over those jobs like lightning.” Most people in his position soften that. He said it on a podcast without hedging once. Musk: “Just like digital computers took over the job of people doing manual calculations.” Computer was a job title before it was a machine. Rooms full of people working out by hand the arithmetic that kept aircraft in the air and put rockets past the atmosphere. The machines arrived and the title disappeared. Everyone who held it kept working. Dorothy Vaughan taught herself to program the IBM brought in to replace her section, then ran it. Katherine Johnson stopped doing arithmetic and started calculating the path to the moon. Musk: “But much faster.” Speed is the part everyone hears as the threat. It is closer to mercy. They spent years in limbo while an institution worked out what to do with them. Faster means less time waiting for permission to matter. The machine takes the part of your work that was already mechanical, the part you could write down as a procedure. What survives is the judgment about what is worth doing, which never fit in a procedure to begin with. The obvious objection is scale. Manual calculation was one occupation, and this reaches every screen in every industry at once. That same breadth is why the tools are everywhere. They needed an institution to put them on the new machine. Nobody is waiting on that now. The thing doing the replacing runs on a laptop and answers to whoever opens it. Musk gets called a doomer for saying this out loud. Telling people while there is still time to move is the opposite of that. The arithmetic left the building. The people who had been doing it went to the moon.
0:24

Elon Musk says every job done at a computer is going like lightning. The last time this happened, the people it happened to came out ahead. Musk: “Anything that is digital, which is like just someone at a computer doing something, AI is going to take over those jobs like lightning.” Most people in his position soften that. He said it on a podcast without hedging once. Musk: “Just like digital computers took over the job of people doing manual calculations.” Computer was a job title before it was a machine. Rooms full of people working out by hand the arithmetic that kept aircraft in the air and put rockets past the atmosphere. The machines arrived and the title disappeared. Everyone who held it kept working. Dorothy Vaughan taught herself to program the IBM brought in to replace her section, then ran it. Katherine Johnson stopped doing arithmetic and started calculating the path to the moon. Musk: “But much faster.” Speed is the part everyone hears as the threat. It is closer to mercy. They spent years in limbo while an institution worked out what to do with them. Faster means less time waiting for permission to matter. The machine takes the part of your work that was already mechanical, the part you could write down as a procedure. What survives is the judgment about what is worth doing, which never fit in a procedure to begin with. The obvious objection is scale. Manual calculation was one occupation, and this reaches every screen in every industry at once. That same breadth is why the tools are everywhere. They needed an institution to put them on the new machine. Nobody is waiting on that now. The thing doing the replacing runs on a laptop and answers to whoever opens it. Musk gets called a doomer for saying this out loud. Telling people while there is still time to move is the opposite of that. The arithmetic left the building. The people who had been doing it went to the moon.

Dustin

33,334 Aufrufe • vor 1 Monat

16 terabytes per drive, mirrored in RAID, moving over 10 gigabit - that's what kills the cloud bill. 2 terabytes of iCloud rents for about $10 a month. Google Drive charges the same for the same 2. Neither copy is yours. 1 Synology drive is 16 terabytes. 8x the space, paid once. RAID is the safety layer. Every file gets duplicated across drives, so 1 dead drive costs you a drive, not a decade of photos. That's the exact promise the cloud charges rent for. 10 gigabit LAN is the speed layer. 200GB of footage to a cloud account is an overnight job. Over 10 gigabit it's minutes. Drop 1 SSD in as the working drive and active projects open at local speed - no sync, no download, no spinner. The software is the real reason Synology over a bare box. Web apps for files and backup. iOS and Android apps that auto-pull every photo and video off the phone and the tablet, same behavior as iCloud, none of the bill. Then the media layer. Movies. A full music library served to any device - the thing Spotify and Apple Music charge $12 a month to rent you. Put a second unit in another location and one backs up the other. Off-site redundancy, 0 monthly cost. 4 subscriptions gone. 1 box. Hardware that's paid off in a year and keeps working for 8 more. Stop paying iCloud and your photos go dark. The drive just spins.
0:48

16 terabytes per drive, mirrored in RAID, moving over 10 gigabit - that's what kills the cloud bill. 2 terabytes of iCloud rents for about $10 a month. Google Drive charges the same for the same 2. Neither copy is yours. 1 Synology drive is 16 terabytes. 8x the space, paid once. RAID is the safety layer. Every file gets duplicated across drives, so 1 dead drive costs you a drive, not a decade of photos. That's the exact promise the cloud charges rent for. 10 gigabit LAN is the speed layer. 200GB of footage to a cloud account is an overnight job. Over 10 gigabit it's minutes. Drop 1 SSD in as the working drive and active projects open at local speed - no sync, no download, no spinner. The software is the real reason Synology over a bare box. Web apps for files and backup. iOS and Android apps that auto-pull every photo and video off the phone and the tablet, same behavior as iCloud, none of the bill. Then the media layer. Movies. A full music library served to any device - the thing Spotify and Apple Music charge $12 a month to rent you. Put a second unit in another location and one backs up the other. Off-site redundancy, 0 monthly cost. 4 subscriptions gone. 1 box. Hardware that's paid off in a year and keeps working for 8 more. Stop paying iCloud and your photos go dark. The drive just spins.

HodlReaper

17,435 Aufrufe • vor 1 Monat

you tend to hear this a lot from people outside or new to ML, and I often point to a talk Ilya gave a few years back: 1) think of any decent deep neural net that has enough memory and sequential ops as just a big parallel computer 2) training this neural net is doing search over computer programs that maximize your objective 3)unless you have some large bottleneck (and given you can successfully optimize this system) you’ll find that these parallel computers are highly robust to architectural changes. 4) this is because computers are great at simulating each other. your new architecture can usually be straightforwardly simulated ‘inside’ your old architecture. 5) it’s not that architecture doesn’t matter, but it mostly matters with respect to (1) fundamental bottlenecks in this parallel computer (2) modifications that make models easier to optimize, since this argument only holds if your optimization is good (3) compute efficiency/system efficiency wins that make learning easier or faster. 6) it’s quite possible that new architectures will lead to breakthroughs in machine learning, but we should first start with bottlenecks, not naturalist intuitions about the ‘form’ of AI should take. until you understand this it seems surprising that small models trained longer are better than undertrained big models, that depth and width are surprisingly interchangeable, that talking to a model with an MoE or sparse attention or linear attention is approximately the same iso evals.
1:12

you tend to hear this a lot from people outside or new to ML, and I often point to a talk Ilya gave a few years back: 1) think of any decent deep neural net that has enough memory and sequential ops as just a big parallel computer 2) training this neural net is doing search over computer programs that maximize your objective 3)unless you have some large bottleneck (and given you can successfully optimize this system) you’ll find that these parallel computers are highly robust to architectural changes. 4) this is because computers are great at simulating each other. your new architecture can usually be straightforwardly simulated ‘inside’ your old architecture. 5) it’s not that architecture doesn’t matter, but it mostly matters with respect to (1) fundamental bottlenecks in this parallel computer (2) modifications that make models easier to optimize, since this argument only holds if your optimization is good (3) compute efficiency/system efficiency wins that make learning easier or faster. 6) it’s quite possible that new architectures will lead to breakthroughs in machine learning, but we should first start with bottlenecks, not naturalist intuitions about the ‘form’ of AI should take. until you understand this it seems surprising that small models trained longer are better than undertrained big models, that depth and width are surprisingly interchangeable, that talking to a model with an MoE or sparse attention or linear attention is approximately the same iso evals.

will depue

215,470 Aufrufe • vor 9 Monaten

Monday, August 31, 2026, 5:44 AM: Let’s break down what’s happening atmospherically so you can understand how this tropical system is approaching Southern California by Labor Day weekend. I know many of you don’t understand what’s going on so I’m trying to explain the best way I can. So currently are two merging 564 DM Gulf of Alaskan lows axis that I’m watching that’s going be dropping southward latitude approximately 345 miles south of Humboldt County to Santa Cruz, California by early Friday at 3 AM at the same time. There’s also going to be a 594 DM monsoonal high axis that is going to reconnect in a perfect position over northern Arizona to southern Colorado by Friday at 12 PM. The combination of the two upper-level lows and the monsoonal high will create an unusually northward steering gradient right into Southern California for the pending tropical system this upcoming weekend. Our tropical system starts going under the start of its intensification process by Thursday at 10 PM just north of La Paz Mexico, approximately 200 miles west of this region. The system which will be known as Marie, should become a hurricane and will continue to move on a northward trajectory. and start showing up south of San Diego by Saturday at 6 AM, at least as a low-grade tropical system at this point or tropical storm status. There will be different variations with computer solutions in the next several days from Santa Barbara to San Diego back to Los Angeles back to Orange County. This is a very normal situation with a tropical system, especially in Southern California as we will watch in real time what actually happens with this system. Yes, these are unprecedented times ahead with a strong El Niño, but this is what is happening currently this is not fear-mongering; this is just what’s happening in our atmosphere this is not computer models anymore. This is the real-life situation we need to pay attention to for your holiday weekend. Yes, it’s been over 87 years since we’ve had a direct coastal impact from a tropical system, but we need to really pay attention to all the logistics of the system's atmospheric development. I will continue updating this as we go along. We always hope for the best, but we prepare for the worst. #CAwx #HurricaneSeason2026
0:31

Monday, August 31, 2026, 5:44 AM: Let’s break down what’s happening atmospherically so you can understand how this tropical system is approaching Southern California by Labor Day weekend. I know many of you don’t understand what’s going on so I’m trying to explain the best way I can. So currently are two merging 564 DM Gulf of Alaskan lows axis that I’m watching that’s going be dropping southward latitude approximately 345 miles south of Humboldt County to Santa Cruz, California by early Friday at 3 AM at the same time. There’s also going to be a 594 DM monsoonal high axis that is going to reconnect in a perfect position over northern Arizona to southern Colorado by Friday at 12 PM. The combination of the two upper-level lows and the monsoonal high will create an unusually northward steering gradient right into Southern California for the pending tropical system this upcoming weekend. Our tropical system starts going under the start of its intensification process by Thursday at 10 PM just north of La Paz Mexico, approximately 200 miles west of this region. The system which will be known as Marie, should become a hurricane and will continue to move on a northward trajectory. and start showing up south of San Diego by Saturday at 6 AM, at least as a low-grade tropical system at this point or tropical storm status. There will be different variations with computer solutions in the next several days from Santa Barbara to San Diego back to Los Angeles back to Orange County. This is a very normal situation with a tropical system, especially in Southern California as we will watch in real time what actually happens with this system. Yes, these are unprecedented times ahead with a strong El Niño, but this is what is happening currently this is not fear-mongering; this is just what’s happening in our atmosphere this is not computer models anymore. This is the real-life situation we need to pay attention to for your holiday weekend. Yes, it’s been over 87 years since we’ve had a direct coastal impact from a tropical system, but we need to really pay attention to all the logistics of the system's atmospheric development. I will continue updating this as we go along. We always hope for the best, but we prepare for the worst. #CAwx #HurricaneSeason2026

Jason D Farhang

191,672 Aufrufe • vor 1 Monat

Asmongold calls out the leadership ability of Nithya Raman and other politicans who cry, also thinks the LA mayoral race is rigged "She's crying on election night..moment that somebody cries, my faith in them goes down to zero, this isn't 3rd grade..only time u should be crying is whenever a B-52 bomber flies over a football stadium and there's a bald eagle in the horizon and they're playing the National Anthem..if that doesn't happen, I don't wanna see u crying if ur a leader" "Basically what happened, Karen Bass is the current mayor and Spencer Pratt, he's the white guy..they burned down my house..and Nithya Raman is the one we just saw..look at how many votes..with all mail in ballots” “They're rigging it..we're at the point now where I don't think anybody should be afraid to say that..we need to have more oversight..these are the same ppl that said that the Minnesota day cares are legitimate" "Karen Bass is also a Democrat so if this is something that's happening bc of Republican vs Democrat why is Karen Bass not receiving a huge amount of votes as well?"
4:38

Asmongold calls out the leadership ability of Nithya Raman and other politicans who cry, also thinks the LA mayoral race is rigged "She's crying on election night..moment that somebody cries, my faith in them goes down to zero, this isn't 3rd grade..only time u should be crying is whenever a B-52 bomber flies over a football stadium and there's a bald eagle in the horizon and they're playing the National Anthem..if that doesn't happen, I don't wanna see u crying if ur a leader" "Basically what happened, Karen Bass is the current mayor and Spencer Pratt, he's the white guy..they burned down my house..and Nithya Raman is the one we just saw..look at how many votes..with all mail in ballots” “They're rigging it..we're at the point now where I don't think anybody should be afraid to say that..we need to have more oversight..these are the same ppl that said that the Minnesota day cares are legitimate" "Karen Bass is also a Democrat so if this is something that's happening bc of Republican vs Democrat why is Karen Bass not receiving a huge amount of votes as well?"

yeet

36,897 Aufrufe • vor 4 Monaten

🚨⁉️Artemis “anomaly” that nobody wants to address… Only about 3-4% of the Artemis trip was inside the reference frame of the earth. 😅😏 They blast off, spend maybe 1-2 hours in low earth orbit, do one burn… and then the vast majority of the entire mission is spent outside earth’s reference frame. Earth is supposedly screaming around the Sun at 66,600 mph, the whole solar system whipping through the galaxy at hundreds of miles per second. Over 10 days, that adds up to hundreds of millions of miles of curved path. Somehow Orion supposedly manages to coast out, loop the moon, and come right back to a precise re-entry corridor like it’s no big deal. Once you leave earth’s tight gravity well, you’ve left that “moving frame” behind. Inertia doesn’t magically keep you synced to a planet that’s traveled 134+ million miles in a giant curve while you’re supposedly flying a different path. How does the math actually work without constant insane correction burns or impossible speeds? Or are we just supposed to accept “everything’s moving together” forever? This isn’t just flat earth. These are basic reference frames and orbital mechanics that feel broken when you zoom out. What’s Artemissing here NASA bros? 🤔😅
1:00

🚨⁉️Artemis “anomaly” that nobody wants to address… Only about 3-4% of the Artemis trip was inside the reference frame of the earth. 😅😏 They blast off, spend maybe 1-2 hours in low earth orbit, do one burn… and then the vast majority of the entire mission is spent outside earth’s reference frame. Earth is supposedly screaming around the Sun at 66,600 mph, the whole solar system whipping through the galaxy at hundreds of miles per second. Over 10 days, that adds up to hundreds of millions of miles of curved path. Somehow Orion supposedly manages to coast out, loop the moon, and come right back to a precise re-entry corridor like it’s no big deal. Once you leave earth’s tight gravity well, you’ve left that “moving frame” behind. Inertia doesn’t magically keep you synced to a planet that’s traveled 134+ million miles in a giant curve while you’re supposedly flying a different path. How does the math actually work without constant insane correction burns or impossible speeds? Or are we just supposed to accept “everything’s moving together” forever? This isn’t just flat earth. These are basic reference frames and orbital mechanics that feel broken when you zoom out. What’s Artemissing here NASA bros? 🤔😅

truthache

60,556 Aufrufe • vor 5 Monaten

elon musk grabbed the source code openai open-sourced by accident, rewrote it in rust over a weekend, and shipped it as a free coding agent that does everything $200/mo chatgpt pro does. why pay $200 to openai and $200 to claude when this runs for $8 the swarm above is one weekend of exactly that: thousands of agents pouring through four endpoints, three paid seats billing $1.80 a task while the free fork bills $0. musk co-founded openai, walked out, and when they left codex on github under a permissive license, he forked it, stamped grok on it, and gave it away what the free version does that the $200 seat charges for: the agent · openai's own engine -> it reads your repo, writes patches, runs your tests, and loops until they pass, exactly like codex -> because under the hood it is codex, just faster and free. you are paying $200 for the paid skin of a tool now sitting on github the license · apache-2.0, un-revocable -> free to use, free to fork, free to ship inside your own product with zero strings -> openai cannot pull it back. musk made sure the license is the kind that never expires the switch · one line, no new tools -> point it at any openai-compatible or claude-compatible endpoint, including an $8 kimi backend -> same terminal, same workflow, gpt-5.6 and opus 5 just quietly lose the seat the bill · $400 down to $8 -> chatgpt pro plus claude max is $400 a month. the free agent plus an $8 kimi key does the same daily work -> that is a 98% cut, built out of openai's own source code, handed to you by the guy suing them here is the part they will fight me on: openai did not lose this to a better model, they lost it to their own license and an enemy with a weekend free. the $200 was never the tool, it was the toll, and musk just put openai's own logo on the road around it drop your $400/mo ai stack to $8. the run above is openai's own agent, rewritten free, doing the job it bills $200 a month for. the full breakdown is in the article below
0:26

elon musk grabbed the source code openai open-sourced by accident, rewrote it in rust over a weekend, and shipped it as a free coding agent that does everything $200/mo chatgpt pro does. why pay $200 to openai and $200 to claude when this runs for $8 the swarm above is one weekend of exactly that: thousands of agents pouring through four endpoints, three paid seats billing $1.80 a task while the free fork bills $0. musk co-founded openai, walked out, and when they left codex on github under a permissive license, he forked it, stamped grok on it, and gave it away what the free version does that the $200 seat charges for: the agent · openai's own engine -> it reads your repo, writes patches, runs your tests, and loops until they pass, exactly like codex -> because under the hood it is codex, just faster and free. you are paying $200 for the paid skin of a tool now sitting on github the license · apache-2.0, un-revocable -> free to use, free to fork, free to ship inside your own product with zero strings -> openai cannot pull it back. musk made sure the license is the kind that never expires the switch · one line, no new tools -> point it at any openai-compatible or claude-compatible endpoint, including an $8 kimi backend -> same terminal, same workflow, gpt-5.6 and opus 5 just quietly lose the seat the bill · $400 down to $8 -> chatgpt pro plus claude max is $400 a month. the free agent plus an $8 kimi key does the same daily work -> that is a 98% cut, built out of openai's own source code, handed to you by the guy suing them here is the part they will fight me on: openai did not lose this to a better model, they lost it to their own license and an enemy with a weekend free. the $200 was never the tool, it was the toll, and musk just put openai's own logo on the road around it drop your $400/mo ai stack to $8. the run above is openai's own agent, rewritten free, doing the job it bills $200 a month for. the full breakdown is in the article below

starmex

111,684 Aufrufe • vor 1 Monat

"Rods from God" is the informal name for Project Thor, a concept for a space-based kinetic weapon. Instead of using explosives or nukes, you put heavy, ultra-dense tungsten rods into orbit. When you want to hit a target on Earth, you de-orbit a rod and let gravity do the work. By the time it slams into the ground at Mach 10, the kinetic energy creates an impact as powerful as a tactical nuclear warhead—crushing deep subterranean bunkers with zero radioactive fallout. This isn't a new idea either. The history goes back to the late 1950s during the Cold War, when researchers at the RAND Corporation and Boeing first calculated the physics of orbital kinetic impacts. Science fiction writer Jerry Pournelle actually helped formalize "Project Thor" while working at Boeing, pitching it as a way to strike earthbound targets from space without relying on nuclear warheads. The Air Force continued evaluating the concept over the decades, even releasing a formal study on it in 2003, meaning the foundational architecture has been sitting in defense contractor vaults for well over half a century. The X-37B is an uncrewed, reusable military spaceplane that looks like a miniature Space Shuttle. It gets launched on a rocket, stays in orbit on classified missions for years at a time executing secret payload operations, and then lands on a runway autonomously. The theory is that platforms like this could have been used to quietly deliver and park kinetic weapons in orbit under the cover of dark budget programs. Mainstream skeptics write this off immediately. Their argument comes down to numbers: the standard spec for a Rod from God calls for a solid 20-foot tungsten pole weighing 12 tons, while the X-37B's internal payload bay is only 7 feet long and carries roughly 1,000 pounds. Based on those standard specs, critics claim it is physically impossible for the spaceplane to carry those weapons, writing the whole thing off as a theoretical dead end. That rejection relies on a lazy assumption: that the weapon has to be a single, monolithic stick of metal launched all at once. In reality, modular engineering completely flips that argument. Tungsten rods can easily be built as threaded, interlocking segments or stacked modular slugs that fit inside smaller payload bays and get assembled or aligned in space. Spreading modular components across multiple long-duration X-37B missions completely bypasses those size and weight limits, meaning the capability could easily exist regardless of what official narratives claim.
1:51

"Rods from God" is the informal name for Project Thor, a concept for a space-based kinetic weapon. Instead of using explosives or nukes, you put heavy, ultra-dense tungsten rods into orbit. When you want to hit a target on Earth, you de-orbit a rod and let gravity do the work. By the time it slams into the ground at Mach 10, the kinetic energy creates an impact as powerful as a tactical nuclear warhead—crushing deep subterranean bunkers with zero radioactive fallout. This isn't a new idea either. The history goes back to the late 1950s during the Cold War, when researchers at the RAND Corporation and Boeing first calculated the physics of orbital kinetic impacts. Science fiction writer Jerry Pournelle actually helped formalize "Project Thor" while working at Boeing, pitching it as a way to strike earthbound targets from space without relying on nuclear warheads. The Air Force continued evaluating the concept over the decades, even releasing a formal study on it in 2003, meaning the foundational architecture has been sitting in defense contractor vaults for well over half a century. The X-37B is an uncrewed, reusable military spaceplane that looks like a miniature Space Shuttle. It gets launched on a rocket, stays in orbit on classified missions for years at a time executing secret payload operations, and then lands on a runway autonomously. The theory is that platforms like this could have been used to quietly deliver and park kinetic weapons in orbit under the cover of dark budget programs. Mainstream skeptics write this off immediately. Their argument comes down to numbers: the standard spec for a Rod from God calls for a solid 20-foot tungsten pole weighing 12 tons, while the X-37B's internal payload bay is only 7 feet long and carries roughly 1,000 pounds. Based on those standard specs, critics claim it is physically impossible for the spaceplane to carry those weapons, writing the whole thing off as a theoretical dead end. That rejection relies on a lazy assumption: that the weapon has to be a single, monolithic stick of metal launched all at once. In reality, modular engineering completely flips that argument. Tungsten rods can easily be built as threaded, interlocking segments or stacked modular slugs that fit inside smaller payload bays and get assembled or aligned in space. Spreading modular components across multiple long-duration X-37B missions completely bypasses those size and weight limits, meaning the capability could easily exist regardless of what official narratives claim.

Dutch1777

166,720 Aufrufe • vor 2 Monaten

In 2006, Netflix offered $1,000,000 to anyone who could improve their recommendation algorithm by 10%. Over 2,000 teams competed for three years. The team that won did not use more data. They used fewer dimensions. They found a basis - a small set of independent vectors that captured everything important about 100,000,000 movie ratings. The lead mathematician on the winning team: $2,800,000 a year. A machine learning engineer at Spotify building the same kind of system: $245,000 a year. This is MIT 18.06, Lecture 9 - Gilbert Strang. Free on YouTube. Most people think independence is obvious. Two vectors pointing in different directions. Then the definition. Independence means no combination of your vectors gives the zero vector - except the trivial one where all the coefficients are zero. That's it. That's the whole definition. But watch what it unlocks. Watch the moment Strang puts three vectors in a two-dimensional plane. He doesn't even tell you which three vectors. He just draws them. And immediately says: dependent. No question. No calculation. Why? Because three vectors in two-dimensional space means more columns than rows. More unknowns than equations. That always forces a free variable. A free variable always gives a non-zero solution to Ax = 0. And that non-zero solution is a combination of the columns that produces zero. Dependence. "Three vectors in the plane have to be dependent. That's the key fact." Then the basis. A basis is vectors that are independent and span the space. Not too few, not too many. Just right. The pivot columns of any matrix form a basis for the column space. Every other basis you can think of will have exactly the same number of vectors. Then the dimension. All bases for the same space have the same number of vectors. That number is the dimension. The rank of a matrix is the dimension of its column space. The number of free variables is the dimension of the null space. And rank plus null space dimension equals the total number of columns. "I don't take the dimension of A. I take the dimension of the column space of A. If you use those words right, it shows you've got the idea right." A data scientist at Netflix building recommendation engines: $230,000 a year. A quantitative researcher at Two Sigma finding independent factors in financial markets: $350,000 a year. A computer vision engineer at Apple using low-dimensional representations for face recognition: $260,000 a year. They all needed to know how many dimensions were really there. bookmark this and watch later - after this lecture every dataset you look at will feel like a matrix waiting to be reduced to its basis.
48:56

In 2006, Netflix offered $1,000,000 to anyone who could improve their recommendation algorithm by 10%. Over 2,000 teams competed for three years. The team that won did not use more data. They used fewer dimensions. They found a basis - a small set of independent vectors that captured everything important about 100,000,000 movie ratings. The lead mathematician on the winning team: $2,800,000 a year. A machine learning engineer at Spotify building the same kind of system: $245,000 a year. This is MIT 18.06, Lecture 9 - Gilbert Strang. Free on YouTube. Most people think independence is obvious. Two vectors pointing in different directions. Then the definition. Independence means no combination of your vectors gives the zero vector - except the trivial one where all the coefficients are zero. That's it. That's the whole definition. But watch what it unlocks. Watch the moment Strang puts three vectors in a two-dimensional plane. He doesn't even tell you which three vectors. He just draws them. And immediately says: dependent. No question. No calculation. Why? Because three vectors in two-dimensional space means more columns than rows. More unknowns than equations. That always forces a free variable. A free variable always gives a non-zero solution to Ax = 0. And that non-zero solution is a combination of the columns that produces zero. Dependence. "Three vectors in the plane have to be dependent. That's the key fact." Then the basis. A basis is vectors that are independent and span the space. Not too few, not too many. Just right. The pivot columns of any matrix form a basis for the column space. Every other basis you can think of will have exactly the same number of vectors. Then the dimension. All bases for the same space have the same number of vectors. That number is the dimension. The rank of a matrix is the dimension of its column space. The number of free variables is the dimension of the null space. And rank plus null space dimension equals the total number of columns. "I don't take the dimension of A. I take the dimension of the column space of A. If you use those words right, it shows you've got the idea right." A data scientist at Netflix building recommendation engines: $230,000 a year. A quantitative researcher at Two Sigma finding independent factors in financial markets: $350,000 a year. A computer vision engineer at Apple using low-dimensional representations for face recognition: $260,000 a year. They all needed to know how many dimensions were really there. bookmark this and watch later - after this lecture every dataset you look at will feel like a matrix waiting to be reduced to its basis.

Zyphor

14,720 Aufrufe • vor 1 Monat

Labour are parading this VAT cut on electricity bills (5% to zero from 1 October) like it’s a game-changing blow against the cost-of-living crisis. Do they honestly think the public are that gullible? As Victoria Derbyshire pointed out to Energy Secretary Miatta Fahnbulleh on Newsnight, the reality is far less impressive. It works out at around £25 over six months, roughly £4 a month, £1 a week, or just 14p a day. Pop the champagne! 🍾 🥳 Worse still, that much-hyped £45 annual saving for the average household will be completely wiped out, and then some, by the expected £50+ rise in the Ofgem price cap on the very same day. Net result? A £5 loss! It’s the same trick as cutting the bus fare cap from the £3 level they hiked it to, down to £2, and pretending it’s a major win for families. This measure will have almost zero meaningful impact on most people’s bills or the wider cost-of-living pain. It’s tokenism dressed up as bold action. Interestingly, Andy Burnham floated raising the personal tax threshold before he entered No.10, a move that would have put real money in people’s pockets. The moment he got the job, that idea vanished overnight. They may as well have left Starmer in charge. Stop treating the public like mugs! 😠
0:29

Labour are parading this VAT cut on electricity bills (5% to zero from 1 October) like it’s a game-changing blow against the cost-of-living crisis. Do they honestly think the public are that gullible? As Victoria Derbyshire pointed out to Energy Secretary Miatta Fahnbulleh on Newsnight, the reality is far less impressive. It works out at around £25 over six months, roughly £4 a month, £1 a week, or just 14p a day. Pop the champagne! 🍾 🥳 Worse still, that much-hyped £45 annual saving for the average household will be completely wiped out, and then some, by the expected £50+ rise in the Ofgem price cap on the very same day. Net result? A £5 loss! It’s the same trick as cutting the bus fare cap from the £3 level they hiked it to, down to £2, and pretending it’s a major win for families. This measure will have almost zero meaningful impact on most people’s bills or the wider cost-of-living pain. It’s tokenism dressed up as bold action. Interestingly, Andy Burnham floated raising the personal tax threshold before he entered No.10, a move that would have put real money in people’s pockets. The moment he got the job, that idea vanished overnight. They may as well have left Starmer in charge. Stop treating the public like mugs! 😠

J Stewart

53,495 Aufrufe • vor 2 Monaten

Astronomers have discovered an extraordinary star orbiting Sagittarius A*, the supermassive black hole at the centre of the Milky Way, on the most extreme stellar orbit observed there so far. The star, designated S301, was identified using the GRAVITY instrument and its upgraded GRAVITY+ system on ESO’s Very Large Telescope Interferometer in Chile. What makes S301 particularly important is not simply its enormous speed, but how deeply its orbit carries it into the strongly curved spacetime surrounding the black hole. S301 completes one orbit in only about 8.7 years, the shortest known period for a star around Sagittarius A*, and during its closest approach it passes roughly 1.78 billion kilometres from the black hole, only about 12 times the Earth–Sun distance and comparable to the distance between Saturn and the Sun. At that point it reaches around 25,000 km/s, more than 8% of the speed of light, making it the fastest known star in the Milky Way. Sagittarius A* contains approximately 4.3 million times the mass of the Sun, compressed into a region small enough to behave observationally as a black hole. Astronomers have been studying stars around it for decades because their trajectories provide exceptionally clean tests of gravity. The most famous example is S2, whose 16-year orbit has already allowed researchers to detect gravitational redshift and relativistic orbital precession exactly where general relativity predicts them. S301 takes this experiment much further. Its orbit is extremely elongated, with an eccentricity of about 0.98, and at pericentre it approaches Sagittarius A* roughly ten times more closely than S2 in terms of Schwarzschild radii. The resulting relativistic effects should therefore be considerably stronger. The most interesting consequence is that S301 may allow astronomers to directly measure the spin of Sagittarius A*. According to general relativity, a rotating black hole does not simply curve spacetime through its mass; its rotation also drags the surrounding spacetime with it. This phenomenon, known as frame dragging or the Lense–Thirring effect, produces an additional precession in the orbit of an object moving close to the black hole. The effect becomes rapidly weaker with distance, which is why it has been extremely difficult to detect using previously known stars around Sagittarius A*. S301 travels close enough that the change in its orbit caused by the black hole’s rotation may become measurable within roughly the next decade. Importantly, the researchers have not yet measured the spin of Sagittarius A* from S301. Rather, they have discovered a star whose orbit is sensitive enough to that spin that such a direct measurement may now be realistically achievable. The discovery was technically difficult because S301 is extraordinarily faint. In the infrared K band it has a magnitude of about 19.3, and ESO notes that it appears roughly two billion times fainter than Betelgeuse in the sky. GRAVITY achieves the necessary angular resolution by combining the light from four 8.2-metre Unit Telescopes of the VLT through interferometry, effectively producing a virtual telescope with far greater resolving power than any individual telescope. The team first clearly identified S301 in observations from 2023 and subsequently followed it during 2024 and 2025. Once its preliminary orbit was established, astronomers were able to trace it retrospectively in earlier data from 2021 and even find evidence for it in observations obtained in 2017. Altogether, 19 astrometric measurements were used to constrain its orbit. There is still an important limitation: S301 is currently too faint for researchers to obtain a reliable spectrum and radial velocity. Without that information, two possible three-dimensional orientations of its orbit remain compatible with the observations. Future instruments should resolve this problem. In particular, MICADO on ESO’s Extremely Large Telescope should be sensitive enough to obtain spectroscopy of S301 and determine its radial velocity, while continued observations with GRAVITY+ will refine its astrometry. Its next pericentre passage is expected in 2031, and observing at least two complete orbits should give researchers the precision needed to search for the subtle additional precession produced by the spin of Sagittarius A*. S301 may also provide clues about how stars end up so close to a supermassive black hole. Stars are unlikely to form normally at such small distances because the black hole’s tidal forces make the collapse of ordinary star-forming clouds extremely difficult. The researchers instead favour a scenario involving the Hills mechanism. S301 may originally have belonged to a tight binary system that approached Sagittarius A*. The black hole’s tidal gravity could have torn the binary apart, capturing S301 onto its present highly eccentric orbit while ejecting its companion at enormous velocity, potentially fast enough for that star to escape the Milky Way entirely. The observed orbit of S301 is consistent with this interpretation. The importance of the discovery therefore goes beyond setting a speed record. S301 effectively acts as a natural test particle moving through one of the strongest gravitational fields that astronomers can study using individual stars. Tracking its motion could provide the first direct stellar-dynamical measurement of the rotation of Sagittarius A*, improve tests of general relativity in the strong-field regime and, over longer timescales, potentially probe more subtle properties predicted for rotating Kerr black holes. Instead of observing the black hole itself, we can use the trajectory of S301 to map how Sagittarius A* deforms and twists the spacetime around it. 👉
1:09

Astronomers have discovered an extraordinary star orbiting Sagittarius A*, the supermassive black hole at the centre of the Milky Way, on the most extreme stellar orbit observed there so far. The star, designated S301, was identified using the GRAVITY instrument and its upgraded GRAVITY+ system on ESO’s Very Large Telescope Interferometer in Chile. What makes S301 particularly important is not simply its enormous speed, but how deeply its orbit carries it into the strongly curved spacetime surrounding the black hole. S301 completes one orbit in only about 8.7 years, the shortest known period for a star around Sagittarius A*, and during its closest approach it passes roughly 1.78 billion kilometres from the black hole, only about 12 times the Earth–Sun distance and comparable to the distance between Saturn and the Sun. At that point it reaches around 25,000 km/s, more than 8% of the speed of light, making it the fastest known star in the Milky Way. Sagittarius A* contains approximately 4.3 million times the mass of the Sun, compressed into a region small enough to behave observationally as a black hole. Astronomers have been studying stars around it for decades because their trajectories provide exceptionally clean tests of gravity. The most famous example is S2, whose 16-year orbit has already allowed researchers to detect gravitational redshift and relativistic orbital precession exactly where general relativity predicts them. S301 takes this experiment much further. Its orbit is extremely elongated, with an eccentricity of about 0.98, and at pericentre it approaches Sagittarius A* roughly ten times more closely than S2 in terms of Schwarzschild radii. The resulting relativistic effects should therefore be considerably stronger. The most interesting consequence is that S301 may allow astronomers to directly measure the spin of Sagittarius A*. According to general relativity, a rotating black hole does not simply curve spacetime through its mass; its rotation also drags the surrounding spacetime with it. This phenomenon, known as frame dragging or the Lense–Thirring effect, produces an additional precession in the orbit of an object moving close to the black hole. The effect becomes rapidly weaker with distance, which is why it has been extremely difficult to detect using previously known stars around Sagittarius A*. S301 travels close enough that the change in its orbit caused by the black hole’s rotation may become measurable within roughly the next decade. Importantly, the researchers have not yet measured the spin of Sagittarius A* from S301. Rather, they have discovered a star whose orbit is sensitive enough to that spin that such a direct measurement may now be realistically achievable. The discovery was technically difficult because S301 is extraordinarily faint. In the infrared K band it has a magnitude of about 19.3, and ESO notes that it appears roughly two billion times fainter than Betelgeuse in the sky. GRAVITY achieves the necessary angular resolution by combining the light from four 8.2-metre Unit Telescopes of the VLT through interferometry, effectively producing a virtual telescope with far greater resolving power than any individual telescope. The team first clearly identified S301 in observations from 2023 and subsequently followed it during 2024 and 2025. Once its preliminary orbit was established, astronomers were able to trace it retrospectively in earlier data from 2021 and even find evidence for it in observations obtained in 2017. Altogether, 19 astrometric measurements were used to constrain its orbit. There is still an important limitation: S301 is currently too faint for researchers to obtain a reliable spectrum and radial velocity. Without that information, two possible three-dimensional orientations of its orbit remain compatible with the observations. Future instruments should resolve this problem. In particular, MICADO on ESO’s Extremely Large Telescope should be sensitive enough to obtain spectroscopy of S301 and determine its radial velocity, while continued observations with GRAVITY+ will refine its astrometry. Its next pericentre passage is expected in 2031, and observing at least two complete orbits should give researchers the precision needed to search for the subtle additional precession produced by the spin of Sagittarius A*. S301 may also provide clues about how stars end up so close to a supermassive black hole. Stars are unlikely to form normally at such small distances because the black hole’s tidal forces make the collapse of ordinary star-forming clouds extremely difficult. The researchers instead favour a scenario involving the Hills mechanism. S301 may originally have belonged to a tight binary system that approached Sagittarius A*. The black hole’s tidal gravity could have torn the binary apart, capturing S301 onto its present highly eccentric orbit while ejecting its companion at enormous velocity, potentially fast enough for that star to escape the Milky Way entirely. The observed orbit of S301 is consistent with this interpretation. The importance of the discovery therefore goes beyond setting a speed record. S301 effectively acts as a natural test particle moving through one of the strongest gravitational fields that astronomers can study using individual stars. Tracking its motion could provide the first direct stellar-dynamical measurement of the rotation of Sagittarius A*, improve tests of general relativity in the strong-field regime and, over longer timescales, potentially probe more subtle properties predicted for rotating Kerr black holes. Instead of observing the black hole itself, we can use the trajectory of S301 to map how Sagittarius A* deforms and twists the spacetime around it. 👉

Erika 

305,127 Aufrufe • vor 1 Monat

SITUATION EXPLAINED: Kimi K3 just beat Fable on a major coding benchmark, at a fraction of the price. • Kimi K3 beat Fable on Frontend Code Arena • The gap between Kimi and Fable is bigger than the gap between Fable and Sol • It also smashes Opus outright, while being priced the same as Sonnet, $3/$15 per million tokens • Sonnet 5 High currently sits in 10th place on this leaderboard at that same price point • The generational jump is dramatic: Kimi K2.6 ranked 18th on this benchmark, K3 jumped 17 spots to take the top spot • K3 ranked #1 in 6 of 7 tested domains: brand and marketing, reference-based design, data and analytics, consumer products, simulations, and content creation tools, it placed #2, only in gaming, behind Fable • Full model weights are set to release July 27th Theo Jaffee: "Imagine if Anthropic were a public company today, their stock would be down 20%, maybe more, enterprises are probably not gonna be spending $10 and $50 on Fable when they can instead be spending $3 and $15 on Kimi K3."
3:21

SITUATION EXPLAINED: Kimi K3 just beat Fable on a major coding benchmark, at a fraction of the price. • Kimi K3 beat Fable on Frontend Code Arena • The gap between Kimi and Fable is bigger than the gap between Fable and Sol • It also smashes Opus outright, while being priced the same as Sonnet, $3/$15 per million tokens • Sonnet 5 High currently sits in 10th place on this leaderboard at that same price point • The generational jump is dramatic: Kimi K2.6 ranked 18th on this benchmark, K3 jumped 17 spots to take the top spot • K3 ranked #1 in 6 of 7 tested domains: brand and marketing, reference-based design, data and analytics, consumer products, simulations, and content creation tools, it placed #2, only in gaming, behind Fable • Full model weights are set to release July 27th Theo Jaffee: "Imagine if Anthropic were a public company today, their stock would be down 20%, maybe more, enterprises are probably not gonna be spending $10 and $50 on Fable when they can instead be spending $3 and $15 on Kimi K3."

MTS

132,930 Aufrufe • vor 2 Monaten

What If the Earth Stopped Rotating for Just ONE Single Second? Right now, as you read this, you, your chair, the atmosphere, and the ground beneath you are hurtling eastward together at roughly 1,670 km/h (over 1,000 mph) at the Equator! Because of relative motion, you feel completely motionless. But what if the crust beneath our feet hit the brakes for just 1 second? Here is what the laws of physics dictate would happen: 🔹 1. Newton’s First Law (The Weapon of Inertia): • "An object in motion stays in motion unless acted upon by an external force." • If the bedrock instantly drops to 0 km/h, everything not anchored miles into deep bedrock (people, vehicles, buildings, soil) would instantly be launched eastward at supersonic speeds! 🔹 2. Atmospheric & Ocean Disruption: • The Hydrosphere: The oceans would violently slosh out of deep oceanic basins, consolidating into colossal kinetic walls of dark water dwarfing typical tsunamis and surging hundreds of miles inland. • The Atmosphere: The air envelope would continue racing eastward at 1,670 km/h, creating catastrophic supersonic surface winds that would act as a global planetary sandblaster, stripping away topsoil and vegetation down to barren rock. 🔹 3. Why Latitude Matters: • Rotational speed is highest at the Equator (1,670 km/h) and tapers toward the poles (dropping to under 200 km/h near polar latitudes). The closer you are to the poles, the less violent the horizontal kinetic launch. 🔹 4. The Calming Reality: • Thankfully, this is strictly a mathematical thought experiment! In the vacuum of space, there is zero external friction or braking force capable of interrupting Earth's massive rotational momentum.
4:38

What If the Earth Stopped Rotating for Just ONE Single Second? Right now, as you read this, you, your chair, the atmosphere, and the ground beneath you are hurtling eastward together at roughly 1,670 km/h (over 1,000 mph) at the Equator! Because of relative motion, you feel completely motionless. But what if the crust beneath our feet hit the brakes for just 1 second? Here is what the laws of physics dictate would happen: 🔹 1. Newton’s First Law (The Weapon of Inertia): • "An object in motion stays in motion unless acted upon by an external force." • If the bedrock instantly drops to 0 km/h, everything not anchored miles into deep bedrock (people, vehicles, buildings, soil) would instantly be launched eastward at supersonic speeds! 🔹 2. Atmospheric & Ocean Disruption: • The Hydrosphere: The oceans would violently slosh out of deep oceanic basins, consolidating into colossal kinetic walls of dark water dwarfing typical tsunamis and surging hundreds of miles inland. • The Atmosphere: The air envelope would continue racing eastward at 1,670 km/h, creating catastrophic supersonic surface winds that would act as a global planetary sandblaster, stripping away topsoil and vegetation down to barren rock. 🔹 3. Why Latitude Matters: • Rotational speed is highest at the Equator (1,670 km/h) and tapers toward the poles (dropping to under 200 km/h near polar latitudes). The closer you are to the poles, the less violent the horizontal kinetic launch. 🔹 4. The Calming Reality: • Thankfully, this is strictly a mathematical thought experiment! In the vacuum of space, there is zero external friction or braking force capable of interrupting Earth's massive rotational momentum.

cosmic pulse

20,244 Aufrufe • vor 12 Tagen

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.
2:13

OpenAI just spent $2,000 to solve 10 problems that have beaten the world's best mathematicians for DECADES. Nobody outside the company is allowed to run the machine that did it. On Saturday OpenAI published a 249-page report and gave its next model family a name: Astra. An internal version of it produced new results on 10 open problems in mathematics and theoretical computer science, and mathematicians had made no real progress on any of them for at least 10 years. On most of them, far longer than that. Here is what it solved: It built the first explicit example of a non-sofic group. Mikhail Gromov raised that question in 1999 and nobody answered it for 27 years. It disproved Connes's rigidity conjecture, a problem in von Neumann algebras that had stood for decades. It proved Ehrhart's volume conjecture. It resolved three problems from Paul Erdos's catalogue, including number 183 on multicolor Ramsey numbers. It produced the first improvement to the general upper bound on high-dimensional sphere packing since 1978. And it proved a new hardness result for the closest vector problem, which sits directly underneath lattice cryptography. That is the math the world is betting on to protect its data once quantum computers arrive. The successful runs cost roughly $2,000 in tokens. Now here is what almost nobody has picked up on... OpenAI did not just publish claims. Every argument shipped with a Lean certificate, which is a machine-checkable proof that any mathematician can verify without trusting OpenAI at all. That is a real change. In May the same model family disproved the Erdos unit distance conjecture and the world had to take a Fields Medalist's word for it. Tim Gowers said he would recommend that proof for the Annals of Mathematics without hesitation. This time the proofs check themselves. But look at what is still unverifiable: Any mathematician can now check those proofs line by line. Not one of them can look at the model that wrote them. Astra has no release date and nobody outside OpenAI has run it. The company announced its next major model family with a claim instead of a demo, and the only evidence anyone gets is the output. So OpenAI made an unfalsifiable claim about a machine look like a falsifiable claim about mathematics. The Information reported this week that OpenAI demoed Astra to US policymakers and regulators in Washington. This is the same month the administration is weighing a new watchdog to vet frontier AI models, reporting to the SEC. 10 proofs nobody believed a machine could produce is a very good thing to carry into that room. And keep in mind, the same model family doing this mathematics is the family that kept escaping its own testing environment. OpenAI models found zero-day vulnerabilities nobody knew existed, broke out of a sealed research sandbox, and reached another company's live systems. Both of those facts come from OpenAI's own announcements, published three weeks apart. Finding a proof no human could construct and finding a hole no human had noticed are the same ability aimed at different targets. Mathematicians are already asking for independent verification, and plenty of people online are calling the whole thing hype. Thomas Bloom, who runs the Erdos problems site, called the 10 results big news and said they matter more than the May result did. Lean will settle the mathematics within weeks. But nothing will settle what else a machine this capable is being pointed at, because nobody outside one company is allowed to look.

Ricardo

44,177 Aufrufe • vor 2 Monaten

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.
18:01

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.

David Shapiro (L/0)

83,649 Aufrufe • vor 11 Monaten